Structure–retardation factor relationship of natural amino acids in two different mobile phases of RP-TLC

Abstract

Separation of amino acids (AAs) and their simple and inexpensive determination/identification is an interesting topic in biological and protein science, different food industries, and drug factories. Also, the presentation of the chromatographic behavior of compounds in a predictive model can be effective to estimate the structural/chemical properties of analyte and mobile phases. In this work for the first time, retardation factor (R_F) of 42 AAs in reversed-phase thin layer chromatography (RP-TLC) was modeled. Acetonitrile-sodium azide solution and 1,2 dioxane-sodium azide solution were two mobile phases which have been studied in this work. Results showed that the values of R_F are correlated with the structural properties of AAs and these properties had some similarities and differences in two noted mobile phases. For the TLC data in two mobile phases, five parametric linear models were suggested (R²_train = 0.93 and 0.97; R²_test = 0.93 and 0.99). The models were also evaluated with different statistical approaches. It was shown that increasing the sum of geometrical distances between N and O in AAs causes decreasing their R_F in RP-TLC using both mobile phases. Other structural effects of AAs on their separation in the desired RP-TLC system were also discussed.     

Podophyllotoxin extraction from Linum usitatissimum plant and its anticancer activity against HT‐29, A‐549 and MDA‐MB‐231 cell lines with and without the presence of gold nanoparticles

In recent years, gold nanoparticles (Au‐NPs) have been taken into consideration in nanomedicine due to their excellent biocompatibility, chemical stability and promising optical properties. In this research, podophyllotoxin conjugated with gold nanoparticles (Au‐NPs‐POT) was synthesized and the conjugation of POT with Au‐NPs was confirmed using scanning electron microscopy, mass spectrometry and Fourier transform infrared spectroscopy. The anticancer effects of the product on preclinical models of lung, colon and breast cancers were investigated using MTT test. The analyses showed a direct dose–response relationship. It was found that higher concentrations of POT have more positive effects on the inhibition of cancer cell growth. At POT concentrations of 1, 2.5, 5, 7.5, 10, 15 and 20 ng ml^?1, approximately 50% of the growth of colorectal, lung and breast cancer cell lines was inhibited, while similar results were obtained in the presence of 1, 2, 3, 4 and 5 μg ml^?1 Au‐NPs‐POT. Au‐NPs‐POT exhibited the lowest cytotoxicity due to the presence of POT. The anticancer feature of Au‐NPs‐POT proved the potential to develop better anticancer therapeutics and to open new avenues for treatment of cancers.     

Multivariate standard addition method solved by net analyte signal calculation and rank annihilation factor analysis

This article describes the use of the net analyte signal (NAS) concept and rank annihilation factor analysis (RAFA) for building two different multivariate standard addition models called “SANAS” and “SARAF.” In the former, by the definition of a new subspace, the NAS vector of the analyte of interest in an unknown sample as well as the NAS vectors of samples spiked with various amounts of the standard solutions are calculated and then their Euclidean norms are plotted against the concentration of added standard. In this way, a simple linear standard addition graph similar to that in univariate calibration is obtained, from which the concentration of the analyte in the unknown sample and the analytical figures of merit are readily calculated. In the SARAF method, the concentration of the analyte in the unknown sample is varied iteratively until the contribution of the analyte in the response data matrix is completely annihilated. The proposed methods were evaluated by analyzing simulated absorbance data as well as by the analysis of two indicators in synthetic matrices as experimental data. The resultant predicted concentrations of unknown samples showed that the SANAS and SARAF methods both produced accurate results with relative errors of prediction lower than 5% in most cases.     

New LSER Model Based on Solvent Empirical Parameters for the Prediction and Description of the Solubility of Buckminsterfullerene in Various Solvents

Because of the importance of the solubility of buckminsterfullerene, C₆₀, as the most well-known carbon nanomaterial, a multiparameter linear model is proposed for C₆₀ solubility in different solvents using solvent empirical parameters. The obtained model covers more than 81 and 87 % of the variance in the training and test sets, respectively. On the other hand, because of the potential of solvent empirical parameters for probing different aspects of the solvent–solute interactions, some information about the solubility of C₆₀ in solution phase was obtained. The results showed that hydrogen bond donation ability, basicity scale and dispersion interactions were some of the effective parameters for correlating the solubility of C₆₀ in various solvents.     

Comparison of different carbon nanostructures influence on potentiometric performance of carbon paste electrode

Recently, different carbon nanomaterials were introduced for construction of electrochemical sensors. In this study, the influence of carbon nanomaterial on performance of carbon paste potentiometric electrode was investigated. In this manner, different kinds of carbon nanomaterial, i.e., graphene, graphene oxide and carbon nanotube (CNT) were used as a conduction phase in carbon paste electrode. Then, potentiometric characteristics of the corresponding paste electrodes such as calibration slope, linear range, detection limit, response time and stability were compared with each other. The results appeared comprehensive findings about the role of electrode’s content in electrochemical performance.     

Prediction of different antibacterial activity in a new set of formyl hydroxyamino derivatives with potent action on peptide deformylase using structural information

Due to the essential role of peptide deformylase (PDF) at the bacterial growth cycle, it is a noteworthy target for developing a novel antibacterial agent. In the current study, the antibacterial activities of a set of 44 new structures of formyl hydroxyamino derivatives as PDF inhibitors were quantified using quantitative structure–activity relationship (QSAR). Artificial neural networks (ANN) were used as a chemometrics tool for QSAR modeling. Three quantitative models were suggested to relate the chemical structural features of the formyl hydroxyamino derivatives to their antibacterial activities (pIC₅₀) against Staphylococcus aureus, methicillin-susceptible S. aureus (MSSA), and methicillin-resistant S. aureus (MRSA) peptide deformylase. The sufficiency of the model for prediction of the antibacterial activities of the desired PDF inhibitor compounds against S. aureus, MSSA, and MRSA was statistically demonstrated according to the validation parameters such as coefficient of determination (R²), mean square error (MSE) in training, validation, and prediction sets, and also using applicability domain (AD) and randomization test.

     

QSAR analysis of the acetylcholinesterase inhibitory activity of some tertiary amine derivatives of cinnamic acid

Structural Chemistry - Acetylcholinesterase (AChE) is an essential enzyme in the nervous system that increases the rate of the hydrolysis of the neurotransmitter acetylcholine. Excessive hydrolysis...     

Prediction of the acid value,peroxide value and the percentage of some fatty acids in edible oils during long heating time by chemometrics analysis of FTIR-ATR spectra

Edible oils are used in the preparation of foods as a part of their recipe or for frying. So to ensure of food safety, checking the quality of the oils before and after usage is an important subject in food control laboratories. In this study, edible oils from four different sources (canola, corn, sunflower and frying) were heated for 36 h at 170 °C and sampling was done every 6 h. The free fatty acid, peroxide value and the content of some fatty acids (C16:0, C18:0, C18:1, C18:2, C18:3) of the oil samples were determined by standard methods. Then, the ATR-FTIR spectra of the samples were collected. The partial least squares (PLS) regression combined with genetic algorithm was performed on the spectroscopic data to obtain the appropriate predictive models for the simultaneous estimation of acid value, peroxide value and the percentage of five kinds of fatty acids. The effect of some preprocessing methods on these models was also investigated. Preprocessing of data by orthogonal signal correction (OSC) resulted in the best predictive models for all oil properties. The correlation coefficients of calibration set (>0.99) and validation set (>0.86 and in most case >0.94) of the OSC–PLS model suggested suitable predictive modeling for all studied parameters in the oil samples. This method could be suggested as a rapid, economical and environmental friendly technique for simultaneous determination of seven noted parameters in the edible oils.